Joint DOD and DOA Estimation for Bistatic MIMO Radar: A Covariance Trilinear Decomposition Perspective
The covariance methods exert an effect on spatially colored (correlated) noise elimination during direction finding in multiple-input multiple-output (MIMO) radar. However, most of the existing methods seem difficulty to achieve a good balance between accuracy and efficiency. This paper aims at form...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8695814/ |
| _version_ | 1818427924720648192 |
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| author | Fangqing Wen Zijing Zhang Gong Zhang |
| author_facet | Fangqing Wen Zijing Zhang Gong Zhang |
| author_sort | Fangqing Wen |
| collection | DOAJ |
| description | The covariance methods exert an effect on spatially colored (correlated) noise elimination during direction finding in multiple-input multiple-output (MIMO) radar. However, most of the existing methods seem difficulty to achieve a good balance between accuracy and efficiency. This paper aims at formulating a covariance trilinear decomposition perspective for direction-of-departure (DOD) and direction-of-arrival (DOA) estimation for bistatic MIMO radar. First, the array covariance matrix model is presented for de-noising. Furthermore, the noiseless covariance matrix is rearranged into a trilinear decomposition model. Finally, joint DOD and DOA estimation are linked to trilinear decomposition, which can be easily accomplished by exploiting the existing COMFAC technique. The proposed scheme can exploit the tensor structure of the covariance matrix, and it is attractive from the perspective of computational complexity. Moreover, it can be easily extended to the spatially colored noise scenario. The proposed algorithm is analyzed in terms of identifiability, flexibility, and complexity, and the stochastic Cramér-Rao bound on joint DOD and DOA estimation is derived. The computer simulations verify the effectiveness and improvement of the proposed method. |
| first_indexed | 2024-12-14T14:53:28Z |
| format | Article |
| id | doaj.art-939ae11193fa44c6b350475468805acf |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T14:53:28Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-939ae11193fa44c6b350475468805acf2022-12-21T22:57:04ZengIEEEIEEE Access2169-35362019-01-017532735328310.1109/ACCESS.2019.29128428695814Joint DOD and DOA Estimation for Bistatic MIMO Radar: A Covariance Trilinear Decomposition PerspectiveFangqing Wen0https://orcid.org/0000-0001-6527-4326Zijing Zhang1Gong Zhang2National Demonstration Center for Experimental Electrical & Electronic Education, Yangtze University, Jingzhou, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an, ChinaKey Laboratory of Radar Imaging and Microwave Photonics, Nanjing University of Aeronautics and Astronautics, Ministry of Education, Nanjing, ChinaThe covariance methods exert an effect on spatially colored (correlated) noise elimination during direction finding in multiple-input multiple-output (MIMO) radar. However, most of the existing methods seem difficulty to achieve a good balance between accuracy and efficiency. This paper aims at formulating a covariance trilinear decomposition perspective for direction-of-departure (DOD) and direction-of-arrival (DOA) estimation for bistatic MIMO radar. First, the array covariance matrix model is presented for de-noising. Furthermore, the noiseless covariance matrix is rearranged into a trilinear decomposition model. Finally, joint DOD and DOA estimation are linked to trilinear decomposition, which can be easily accomplished by exploiting the existing COMFAC technique. The proposed scheme can exploit the tensor structure of the covariance matrix, and it is attractive from the perspective of computational complexity. Moreover, it can be easily extended to the spatially colored noise scenario. The proposed algorithm is analyzed in terms of identifiability, flexibility, and complexity, and the stochastic Cramér-Rao bound on joint DOD and DOA estimation is derived. The computer simulations verify the effectiveness and improvement of the proposed method.https://ieeexplore.ieee.org/document/8695814/Array signal processingbistatic MIMO radardirection findingspatially colored noise |
| spellingShingle | Fangqing Wen Zijing Zhang Gong Zhang Joint DOD and DOA Estimation for Bistatic MIMO Radar: A Covariance Trilinear Decomposition Perspective IEEE Access Array signal processing bistatic MIMO radar direction finding spatially colored noise |
| title | Joint DOD and DOA Estimation for Bistatic MIMO Radar: A Covariance Trilinear Decomposition Perspective |
| title_full | Joint DOD and DOA Estimation for Bistatic MIMO Radar: A Covariance Trilinear Decomposition Perspective |
| title_fullStr | Joint DOD and DOA Estimation for Bistatic MIMO Radar: A Covariance Trilinear Decomposition Perspective |
| title_full_unstemmed | Joint DOD and DOA Estimation for Bistatic MIMO Radar: A Covariance Trilinear Decomposition Perspective |
| title_short | Joint DOD and DOA Estimation for Bistatic MIMO Radar: A Covariance Trilinear Decomposition Perspective |
| title_sort | joint dod and doa estimation for bistatic mimo radar a covariance trilinear decomposition perspective |
| topic | Array signal processing bistatic MIMO radar direction finding spatially colored noise |
| url | https://ieeexplore.ieee.org/document/8695814/ |
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